Neural networks as "software sensors" in enzyme engineering.

Abstract

Industrial applications of enzyme technology are rapidly increasing. On-line control of enzyme production processes, however, is difficult owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables and quality attributes. We demonstrate that well-trained feedforward backpropagation neural networks with one hidden layer can be employed to overcome such problems with no need for a priori knowledge of the relationships of the process variables involved. Neural network programs were written in Microsoft Visual C++ for Windows and implemented in a personal computer. The goodness of fit of the trained neural network to the reference data was determined by the coefficient of determination, R2. Case studies of beta-galactosidase, glucoamylase, lipase, and xylanase production processes will be used as examples.